I Built a Neural Gate for My AI Agent — Layer 2 of Self-Verification
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Neural gate for self-verification in AI agents is novel and highly relevant to agent architecture.
Yuhao Lin built GateGuard, a three-layer verification stack for AI agents that replaces file-system checks with neural gates measuring constraint penetration, not just arrival. Layer 1 mechanical gates validated across 150 tasks cut violation rates from 55.9% to 0.7%, while Layer 2 neural gate v2 uses DeepSeek's logprobs=True to compute token probability deltas (>0.3 units) indicating active influence. Roadmapped v3 trains linear probes on Qwen2.5-1.5B residual streams to detect early decay, addressing that 7 audited frameworks lack neural-layer fidelity checking and 34 growth-logs showed 55.9% violations pre-GateGuard.